CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data

Kalina Borkiewicz; Viraj Shah; J. P. Naiman; Chuanyue Shen; Stuart Levy; Jeff Carpenter

doi:10.1109/VIS49827.2021.9623327

CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data

Computer Vision and Pattern Recognition 2024-11-01 v1 Graphics Machine Learning Image and Video Processing

Authors: Kalina Borkiewicz , Viraj Shah , J. P. Naiman , Chuanyue Shen , Stuart Levy , Jeff Carpenter

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Abstract

Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.

Keywords

remote sensing image classification image processing

Cite

@article{arxiv.2110.13819,
  title  = {CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data},
  author = {Kalina Borkiewicz and Viraj Shah and J. P. Naiman and Chuanyue Shen and Stuart Levy and Jeff Carpenter},
  journal= {arXiv preprint arXiv:2110.13819},
  year   = {2024}
}

CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data

Abstract

Keywords

Cite

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